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This chapter investigates the behavior of Reddit’s news subreddit users and the relationship between their sentiment on exchange rates. Using graphical models and natural language processing, hidden online communities among Reddit users are discovered. The data set used in this project is a mixture of text and categorical data from Reddit’s news subreddit. These data include the titles of the news pages, as well as a few user characteristics, in addition to users’ comments. This data set is an excellent resource to study user reaction to news since their comments are directly linked to the webpage contents. The model considered in this chapter is a hierarchical mixture model which is a generative model that detects overlapping networks using the sentiment from the user generated content. The advantage of this model is that the communities (or groups) are assumed to follow a Chinese restaurant process, and therefore it can automatically detect and cluster the communities. The hidden variables and the hyperparameters for this model are obtained using Gibbs sampling.

The chapter is structured as follows: in the first part, we provide the framework for the analysis of the formation of the born global firm, whereas the entrepreneurial, strategic, and network-based factors are conceptually linked and leading toward a global champion. The analytical model proposes the analysis of strategic choices as defining factors at the level of entrepreneurial behavior, firm strategy, and network. The case study methodology is provided in the second part of the chapter. The third part provides the empirical linkages of entrepreneurial, strategy based, and network factors’ manifestations and underpinnings in R&D intensive entrepreneurial born global firms. These are followed by discussion and conclusions enclosing empirically grounded framework that explains the emergence of R&D intensive entrepreneurial-hidden champions from the perspective of entrepreneurial firm and network theories.

This study aims to provide a systematic literature review of the research study in the area of limit order book (LOB) mechanism of trading and its implications for market efficiency. The study attempts to document the recent theoretical developments and empirical findings from the literature exhaustively and identifies the research gaps for future research.

The study uses seven reputable databases to select 2,514 research studies spanning over 1981-2018 (finally compressed to a pool of 103 articles, based on relevance and impact). The study uses bibliometric network visualization and text analytics to categorize and examine the literature. The chosen articles are compiled and analyzed to provide a comprehensive account of the current research on LOBs.

The recent LOB literature is summarized on various criteria as follows: sub-areas, the types of economies and markets, methodologies and the LOB measures. The review identifies a dearth of studies on the LOBs in emerging markets. It suggests the potential research areas as intraday studies in emerging LOB markets; application of market indicators based on deeper levels of LOB, beyond the best prices; market fragmentation, order routing decision and its impact on order execution quality; optimal display of LOB levels; liquidity dynamics in quote-driven markets vis-à-vis LOB markets; effect of high-frequency trading on market microstructure; application of advanced techniques (e.g. machine learning models, zero-intelligent models); relationship between the trading speed, order aggressiveness, shape and resilience of the order book and informed trading; and information content of the auxiliary order submission strategies, including cancellation, amendments and hidden orders.

For the past 15 years, to the best of the knowledge, a comprehensive review of the literature on LOBs has not been published. The financial markets have transformed significantly over this period, driven by the adoption of LOBs, low latency trading and technological advancements in information dissemination. This article provides an extensive collection and classification of the literature on LOBs. This would be useful for the practitioners, future researchers and academics in the area of financial markets.

With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

The purpose of this paper is to discover social communities from the social networks by propagating affinity messages among members in a localized way. The affinity between any two members is computed by grey relational analysis method.

First, the responsibility messages and the availability messages are restricted to be broadcasted only among a node and its neighbours, i.e. the nodes that connected to it directly. In this way, both the time complexity and the space complexity can be reduced to be near linear to the network size. The near-linear time and space complexity is quite important for social network analysis because social networks are generally very large. Second, instead of the widely used Euclidean distance, the grey relational degree is adopted in the calculation of node similarity, because the latter is more suitable for the discovery of the hidden relations among the nodes. On the basis of the two improvements, a new social community detection algorithm is proposed. Finally, experiments are conducted to verify the performance of the new algorithm.

The new algorithm is evaluated by the experiments on both the real-world and the artificial data sets. The experimental results prove the proposed algorithm to be quite effective and efficient at community discovery.

The algorithm proposed in the paper can be applied to discover communities in many social networks. After the recognition of the social communities, the authors can send advertisements, spot valuable customers or locate criminals more precisely.

The new algorithm revises the affinity propagation progress to be localized to improve both time and space complexity. Furthermore, the grey relational analysis is applied to solve the complex relations among members of the social networks.

Mobile Service of Big Data can be supported by the fused technologies of computing, communication and digital multimedia. The purpose of this paper is to propose new agent-based proactive migration method and system for Big Data Environment (BDE).

First, the authors have designed new relative fusion method for making decision based on fuzzy-neural network. The method can make the fusion belief degree to be improved. Then the authors have proposed agent-based proactive migrating method with service discovery and key frames selection strategy. Finally, the authors have designed the application system, which can support proactive seamless migration function for big data. The method has innovation in which mobile service task of big data can dynamically follow its mobile user from one device to another device.

The authors have proposed agent-based proactive migrating method with service discovery and key frames selection strategy. The method has innovation in which mobile service task of big data can dynamically follow its mobile user from one device to another device. The designed system is convenient to work and use during mobility, and which is useful or helpful for mobile user in the BDE.

The authors have clarified and realizes how to transfer service tasks among different distances in Big Data Environment (BDE). The authors have given a formal description and classification of the mobile service task, which is independent of the realization mechanism. In the designed and developed application system, the new idea adopts fuzzy-neural control theory to make decision for task-oriented proactive seamless migration application.

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

The purpose of this paper is to analyse the paradigm shift of library automation to resource discovery by exploring the applications of resource discovery. The present status of India on adapting resource discovery applications is discussed.

An evaluative method to examine the status quo of India automation and resource discovery scenario is drawn with a related literature review. Moreover, various pertinent global challenges of embracing discovery tools in the digital environment are highlighted.

The growth of the Indian library automation industry is booming. However, library software adaptation, next‐generation catalogue enhancements and community development avenues are dearth, seemingly remote and far from satisfactory.

The paper focuses on the emerging scenario of resource discovery applications with an overview of global challenges.

Neural networks, which provide the basis for deep learning, are a class of machine learning methods that are being applied to a diverse array of fields in business, health, technology, and research. In this chapter, we survey some of the key features of deep neural networks and aspects of their design and architecture. We give an overview of some of the different kinds of networks and their applications and highlight how these architectures are used for business applications such as recommender systems. We also provide a summary of some of the considerations needed for using neural network models and future directions in the field.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.